"You might also like"
- The technological consumers understanding of transparent AI
June 2021
Master's thesis
Master's thesis Emilie Kristin Haugstulen
2021Emilie Kristin Haugstulen NTNU Norwegian University of Science and Technology Faculty of Economics and Management Department of International Business
"You might also like"
- The technological consumers understanding of transparent AI
Emilie Kristin Haugstulen
AE511816 1 Master Thesis International Business - discipline oriented Submission date: June 2021
Supervisor: Mark Pasquine Co-supervisor: Lena Vatne Bjørlo
Norwegian University of Science and Technology
Preface
This thesis represents the end of my Master of Science in International Business and
Marketing at NTNU Ålesund. The past two years has been challenging and difficult, however it has been an interesting and a great learning experience. The research topic is based on a great personal interest of AI and an awareness that this technology will be more present in the years to come.
First, I would like to thank Lena V. Bjørlo for sharing all her knowledge about AI in marketing and taking the time to make this experiment with me. It was inspiring to be introduced to how AI influences our autonomy and that we as consumers should strive for more transparent algorithms. Therefore, I am deeply grateful for this partnership with Lena. I am also grateful for all the respondents providing the necessary data, so we were able to carry out our research.
Most importantly, I would like to thank my supervisor Mark Pasquine. His guidance and support have been helpful through this process, giving constructive feedback and sharing his valuable knowledge and expertise.
Ålesund, June 2021
Emilie K. Haugstulen
Summary
Artificial intelligence is becoming an important tool in digital marketing employed by marketers to influence consumer in an online decision-making process. Recommendation algorithms can provide consumers with better products to fulfill needs and preferences based on their online behavior. However, consumers are rarely given information or explanations on which data that are used, and this could be solved by transparency in the algorithm. This thesis addresses how consumers tech competence might affect how they understand
transparency in algorithms, and their awareness towards information privacy risks with using new age technology. Hereunder, hypotheses are presented to investigate how tech
competence influences consumers understanding of transparency and their privacy awareness. In addition, it is explored if transparency is more important for high identity- relevant products. An experimental design was conducted to explore this topic and attempt to give a better understanding of transparency in algorithms. It was used three experimental conditions where consumers were exposed to different levels of transparency through an online survey. A total of 227 respondents were collected.
From the statistical analysis conducted in SPSS, results indicated that high tech competence consumers understand recommendation algorithm despite the transparency. It was also found that tech competence was positively related to consumers privacy awareness. Contradictive to the assumptions, it was found that transparency is not more important for high identity-
relevant products. Furthermore, it is suggested that regulations and guidelines creating more transparent algorithms is necessary, to protect private information and improve customer experience. To the end of the thesis, it is discussion about findings, implications, limitations and suggestions for further research on the topic presented.
Keywords: Artificial intelligence; Transparency; Tech competence; Online decision-making;
Privacy awareness; Identity-relevance
Author contributions: Conceptualization: L.B.; Introduction: E.H.; Literature review: E.H.;
Hypotheses: E.H., L.B; Experimental design: L.B., M.P.; Survey structure: E.H., L.B.;
Survey execution: E.H., L.B.; Discussion and implications: E.H. Main author E.H. with contributions from L.B.
Norsk sammendrag
Kunstig intelligens begynner å bli et viktig verktøy i digital markedsføring brukt av markedsførere for å påvirke forbrukerne i en online beslutningsprosess.
Anbefalingsalgoritmer kan gi forbrukerne bedre produkter for å oppfylle behov og
preferanser basert på deres online atferd. Imidlertid får forbrukerne sjelden informasjon eller forklaringer på hvilke data som brukes, og dette kan løses ved åpenhet i algoritmen. Denne oppgaven tar for seg hvordan forbrukernes teknologiske kompetanse kan påvirke hvordan de forstår gjennomsiktighet i algoritmer, og deres bevissthet rundt informasjonssikkerhetsrisiko ved bruk av moderne teknologi. Nedenfor presenteres hypoteser for å undersøke hvordan teknologikompetanse påvirker forbrukernes forståelse av åpenhet og deres
personvernbevissthet. I tillegg undersøkes det om gjennomsiktighet er viktigere for produkter med høy relevans for identitet. Et eksperiment design ble utført for å utforske dette emnet og forsøke å gi en bedre forståelse av gjennomsiktighet i algoritmer. Det ble brukt tre
eksperimentelle forhold der forbrukere ble utsatt for forskjellige nivåer av gjennomsiktighet gjennom en online undersøkelse. Totalt 227 respondenter ble samlet inn.
Fra den statistiske analysen som ble utført i SPSS, indikerte resultatene at høyteknologisk kompetente forbrukere forstår anbefalingsalgoritme til tross for gjennomsiktighet. Det ble også funnet at teknisk kompetanse var positivt relatert til forbrukernes personvern. I strid med antagelsene ble det funnet at åpenhet ikke er viktigere for produkter med høy relevans for identitet. Videre foreslås det at forskrifter og retningslinjer for å skape mer gjennomsiktige algoritmer er nødvendig for å beskytte privat informasjon og forbedre kundeopplevelsen. I slutten av oppgaven er det diskusjon om funn, implikasjoner, begrensninger og forslag til videre forskning om temaet som presenteres.
Nøkkelord: Kunstig intelligens; Åpenhet; Teknisk kompetanse; Online beslutningstaking;
Bevissthet om personvern; Identitetsrelevanse
Table of contents
List of figures and tables ... 6
1. Introduction ... 8
1.2 Structure ... 9
2. Literature review ... 9
2.1 Digital marketing ... 9
2.2 Customer experience ... 11
2.2.1 Online decision-making process ... 12
2.3 Consumer autonomy ... 13
2.4 Artificial intelligence (AI) ... 14
2.4.1 Big data ... 14
2.4.2 Algorithms ... 15
2.4.3 Machine learning ... 15
2.4.4 Application of AI and recommendation algorithms ... 16
2.5 Tech competence ... 17
2.6 Transparency ... 18
2.7 Privacy awareness ... 20
2.8 Identity relevance ... 21
3. Methodology ... 22
3.1 Research design ... 23
3.1.1 Experimental design ... 23
3.2 Data collection ... 24
3.2.1 Sawtooth software ... 24
3.2.2 Respondents ... 25
3.2.3 Pretest ... 25
3.2.4 Pilot study ... 26
3.2.5 Main study ... 26
3.3 Measure assessment and data validity ... 27
3.3.1 Data cleaning ... 27
3.3.2 Test of normality ... 27
3.4 Description of variables ... 28
3.4.1 Transparency: experimental conditions ... 29
3.4.2 Transparency: manipulation check ... 29
3.4.3 Perceived autonomy ... 29
3.4.4 Privacy awareness ... 30
3.4.5 Identity-relevance ... 30
3.4.6 Tech competence ... 30
3.4.7 Demographics ... 31
3.5 Descriptive statistics ... 31
3.6 Reliability ... 33
3.7 Validity ... 34
4. Results ... 36
4.1 One-way ANOVA of Tech competence ... 36
4.2 One-way ANOVA of Transparency ... 37
4.3 One-way ANOVA of Privacy Awareness ... 38
4.4 One-way ANOVA of Identity-relevance ... 39
4.5 One-way ANOVA of Consumer autonomy ... 40
4.6 Two-way ANOVA of Tech competence and Transparency ... 41
4.7 Two-way ANOVA for Tech competence and Privacy awareness ... 43
4.8 Two-way ANOVA of Identity-relevance ... 44
4.9 Summary of hypotheses ... 45
5. Discussion ... 46
5.1 The effects of tech competence on transparency ... 46
5.2 The effects of tech competence on privacy awareness ... 48
5.3 The effects of transparency on identity-relevant products ... 49
6. Implications and limitations ... 50
6.1 Theoretical implications ... 50
6.2 Managerial implications ... 51
6.3 Policy implications ... 51
6.4 Research limitations ... 52
6.5 Further research ... 53
7. Conclusions ... 53
References ... 55
Appendix ... 58
Appendix 1: Survey structure ... 58
Appendix 2: Experimental conditions ... 62
Appendix 3: SPSS output ... 68
List of figures and tables
Figure 1: Histogram of Tech competence ... 33
Table 1: Descriptive statistics of pretest ... 26
Table 2: Test of Normality ... 28
Table 3: Descriptive statistics of Demographics ... 31
Table 4: Descriptive statistics of main variables ... 32
Table 5: Reliability statistics ... 34
Table 6: Test of Homogeneity of Variances and ANOVA of Tech competence ... 37
Table 7: Test of Homogeneity of Variances and ANOVA of Transparency shoes ... 38
Table 8: Test of Homogeneity of Variances and ANOVA of Transparency toothpaste ... 38
Table 9: Test of Homogeneity of Variances and ANOVA of Privacy awareness ... 39
Table 10: Test of Homogeneity of Variances and ANOVA of High Identity relevance ... 40
Table 11: Test of Homogeneity of Variances and ANOVA of Low Identity relevance ... 40
Table 12: Test of Homogeneity of Variances and ANOVA of Consumer autonomy ... 41
Table 13: Descriptive Statistics for Transparency conditions ... 42
Table 14:Two-way ANOVA of Tech competence and Transparency Shoes ... 43
Table 15: Two-way ANOVA of Tech competence and Transparency Toothpaste ... 43
Table 16: Two-way ANOVA of Tech competence and Privacy awareness ... 44
Table 17: Two-way ANOVA of Transparency and High Identity-relevance ... 45
Table 18: Two-way ANOVA of Transparency and Low Identity-relevance ... 45
Table 19: Summary of hypotheses ... 46
Appendix 1: Survey structure ... 61
Appendix 2: Shoes - low condition ... 62
Appendix 3: Shoes - medium condition ... 63
Appendix 4: Shoes - high condition ... 64
Appendix 5: TP - low condition ... 65
Appendix 6: TP - medium condition ... 66
Appendix 7: TP - high condition ... 67
Appendix 8: Pretest ... 68
Appendix 9: Descriptive statistics demographics ... 68
Appendix 10: Descriptive statistics variables ... 68
Appendix 11: Test of Normality ... 69
Appendix 12: ANOVA tech competence ... 70
Appendix 13: ANOVA transparency shoes ... 71
Appendix 14: ANOVA transparency TP ... 72
Appendix 15: ANOVA Privacy awareness ... 73
Appendix 16: ANOVA High Identity-relevance ... 74
Appendix 17: ANOVA Low Identity-relevance ... 75
Appendix 18: ANOVA Consumer autonomy ... 76
Appendix 19: Univariate Tech and Transparency Shoes ... 77
Appendix 20: Univariate Tech competence and transparency TP ... 78
Appendix 21: Univariate Tech competence and Privacy Awareness ... 78
Appendix 22: Transparency and identity-relevance Shoes ... 79
Appendix 23: Transparency and identity-relevance TP ... 79
Appendix 24: Reliability Consumer autonomy ... 80
Appendix 25: Reliability transparency combined ... 80
Appendix 27: Reliability Transparency TP ... 81
Appendix 28: Reliability Tech competence ... 81
Appendix 29: Privacy awareness ... 82
Appendix 30: Reliability Identity-relevance ... 82
1. Introduction
Imagine entering a website and finding a product that catch your interest. Further down you notice recommendations made for you and you read the following words: “you might also like”. This scenario is familiar for most users of online services. Despite this, a lot of people are not aware of the influence this might have on us as consumers. Artificial intelligence (AI) and algorithms were first introduced in the 1950s, but the recent development of new age technology including social media and online shopping, has made AI an important part of digital marketing. Marketing has for centuries attempted to influence consumers in a decision-making process, and with new age technology the marketers are given new possibilities to connect with potential consumers and improve the customer experiences.
Recommendation algorithms detect patterns, so the marketers can offer better services and get the ability to understand their customers’ needs and preferences in advanced ways. Also, it can help consumers navigate through choice overload and reduce search costs (Bjørlo, Moen, & Pasquine, 2021). However, algorithms use private and historical data to predict which products that will benefit the consumer, but the consumers are rarely given
explanations of how their online behavior affects the personalized recommendations (Turilli
& Floridi, 2009). Transparency in recommendations can provide information to the consumer about which information that have been used by the algorithm. This is one of the most
important research fields today attempting to understand how the use of AI in marketing influences consumers decision-making, and that transparency in algorithms might improve consumer autonomy.
Simultaneously as AI and algorithms are being developed and improved, consumers are comprehending and using technology like never before. Based on this, consumers are
becoming more tech competent which indicates that most consumers might have the potential to understand what AI is and how this works. Tech competent consumers might also be more aware of the information privacy risks with using new age technology, since algorithms use private and historical data for its recommendations. There is limited research on how
consumers tech competence affects their understanding of transparency in recommendations, additionally how tech competence can affect their awareness towards information privacy risks connected to online services.
In this study we seek to advance our understanding on how consumers tech competence can
them more aware of the information privacy risks. At the end, the study will attempt to see if transparent algorithms are more important for high identity-relevant products.
1.2 Structure
The study has the following structure:
Chapter two contains the literature review where the theoretical framework and hypotheses is presented.
Chapter three provides a description of the methodology, data collection and data cleaning.
Chapter four presents the results and analysis from the experiment.
Chapter five discuss the findings from the previous chapter.
Chapter six present implications and limitations of the study.
Chapter seven includes the concluding remarks.
2. Literature review
This chapter reviews the literature on important topics within digital marketing, artificial intelligence and consumer behavior, forming the theoretical foundation of the thesis. Firstly, a brief introduction to digital marketing and how this has enabled new marketing tools is explained. Secondly, artificial intelligence and recommendation algorithms is explained, and further how this affects consumers autonomy and privacy awareness concerning
recommendation algorithms. Three hypotheses will be presented.
2.1 Digital marketing
Marketing has existed for centuries and has always had the same purpose to influence people into making a decision. It is all about to persuade a consumer to take that action we want them to and choose the product we advertise (Ryan, 2016). To be able to succeed with influencing people and distribute goods and services, marketers need to be good at planning, implementation and follow-up of the activities that are put to action (Vikøren, 2020). Only then a company will successfully satisfy a customer’s need with their products or services offered.
Marketers have since the very beginning found a way of influencing people with the tools available at the time, but it has changed since the origin (Ryan, 2016). A strong tool has been the word-of-mouth, then more tools have become available when new technology has
emerged, such as flyers, radio advertisement, TV-advertisements, e-mails and now: social media (Chaffey & Ellis-Chadwick, 2019). Social media and digital platforms are changing how businesses can communicate and share their message in more efficient ways. Digital marketing has created more tools for marketing including paid search placement, search optimization, pay-per-click advertisement, rich media and social media advertisement
(Chaffey & Ellis-Chadwick, 2019). These keywords are all a part of an online revolution that have led to a new way for businesses to connect with new consumers, those who use and integrate technology into their everyday lives in ways that we could never have conceived a few decades ago (Ryan, 2016).
Social media and digital platforms i.e., Facebook, LinkedIn, Twitter and Instagram are some of the communication tools which effectively used to get the consumers interest
(Balakrishnan, 2018). These platforms help marketers to get a foothold in the market, find new ways to become popular and take market share. Most importantly it is all about the people, which means marketers connect with customers to build trustful relationships and drive sales (Ryan, 2016). This has been the main concept of marketing of all times, but new technology is giving new possibilities for marketers to connect with potential customers worldwide. From this it is possible to define digital marketing as “achieving marketing objectives through applying digital technologies and media” (Chaffey & Ellis-Chadwick, 2019). Internet and digital platforms have transformed marketing giving access to billions of online users who regularly use online platforms and social media to find products,
entertainment and friends.
For companies to succeed in the future, they will need to adapt to the technological changes and apply this in their digital marketing plans (Chaffey & Ellis-Chadwick, 2019). A firm must make an effort to acquire an understanding of their customers’ needs and behaviors across digital platforms using the technological tools available (Kumar, Ramachandran, &
Kumar, 2021). A technological tool which is commonly used in digital marketing, is artificial intelligence (AI). Artificial intelligence is believed to transform business practices,
increasingly changing how administrative planning processes are executed in both marketing,
intelligence and can help humans to make better decisions. It can provide marketers with greater information about consumers and be able to provide better solutions for them (Gentsch, 2018).
AI has transformed many fields, and in marketing the interactions between firms and consumers are increasingly more individualized and generate a lot of big data (Ma & Sun, 2020). This data that consumers leave behind have driven companies to invest in machine learning that can be used to enhance the marketing capabilities. For consumers AI often reveal it selves through i.e., recommendations on e-commerce websites and content platforms such as Amazon and Netflix, deep learning engines who analyze and tag the billions of images on social media sites, automated bidding algorithms who examine a web surfer’s profile in millisecond timescale to determine the optimal bid for ad delivery, and chatbots in customer service (Ma & Sun, 2020).
2.2 Customer experience
To succeed, a company must also meet the demand and satisfy customers’ needs and preferences, therefore create the best customer experience with focus on creating loyalty, value and a good journey (Chaffey & Ellis-Chadwick, 2019). Digital marketing is evolving to become more of a conversation, where marketers interact with the targeted segment, listen to opinions and participate. This can for example be through user-generated content where the marketer can increase the engagement with customers to increase loyalty, and further
increase sales (Chaffey & Ellis-Chadwick, 2019). User-generated content is when consumers can freely create, share and exchange information and ideas in a virtual community which enable communication between consumers and firms (Chaffey & Ellis-Chadwick, 2019). The online presence of brands has increased in the past decades to improve customer relationships (Balakrishnan, 2018). Increasing marketing activities to have more effective communication with consumers, due to their possibilities to investigate products and services and share feedback with other consumers (Balakrishnan, 2018). User-generated content is a powerful tool for the consumers, being able to share honest and open thoughts about how a product, service or firm performs. This can be used to improve products, business models and values which in turn can help improve marketing strategies and strengthen customer relationships (Balakrishnan, 2018).
A customer journey can be defined as touchpoints or different types of paid, owned and earned media which influence consumers as they access different types of website and content when selecting products and services (Chaffey & Ellis-Chadwick, 2019). To create the best customer journey, the marketer needs to focus on creating customer loyalty which is the desire the customer has to continue doing business with a supplier (Chaffey & Ellis- Chadwick, 2019). One of the ultimate goals of interacting and influencing its customers through digital platforms is to create customer loyalty and satisfaction. It is two main drivers to create loyalty, whereas the first one is emotional loyalty and the other is behavioral loyalty.
Emotional loyalty occurs when the loyalty to the brand is demonstrated by favorable perceptions, opinions and recommendations (Chaffey & Ellis-Chadwick, 2019). This gives companies unique insight in customer preferences.
With new-age technology, the consumer expects experiences that are effortless, intuitive, and seamless across touchpoints (Kumar et al., 2021). Therefore, it is important for firms to apply these technologies to their strategies to make an effortless and great experience for the
consumer, to meet and exceed the expectations from the customer (Kumar et al., 2021). This is important if the firm wants the customer to repurchase a product at a later stage or engage in user-generated content for others to see.
2.2.1 Online decision-making process
The Internet of Things (IoT) has opened up a world full of products and services for online customers to choose from, which have left the online decision-making process complex (Kumar et al., 2021). An increasing number of consumers are engaged in online shopping, as well as the number of product options have increased (Karimi, Papamichail, & Holland, 2015). Customer are becoming more prone to shop online, and are also more knowledgeable and demanding since the new age technology provide them with more information (Chaffey
& Ellis-Chadwick, 2019). Therefore, the online decision-making process is becoming more complex for the marketers, since brand, websites, social media and user-generated content needs to align with the customer experience (Chaffey & Ellis-Chadwick, 2019).
2.3 Consumer autonomy
Consumer autonomy can be defined as the right of consumers to make their own decision (N.
C. Smith, Goldstein, & Johnson, 2013). As individuals, we have a need to feel that we are making decisions that will fulfill our needs based on our own preferences, and experience that we have the full freedom to make these choices without feeling constrained or coerced (Matthew, 2006). This is among the most central values and rights consumers have in today's democratic society, given their ability to make well-informed choices (Bjørlo et al., 2021).
With the rapid advancements in new age technology, a marketer’s ability to track, monitor, recommend and predict consumer choices has become better (Wertenbroch et al., 2020). In addition, the internet has reduced search and transaction costs for consumers, leaving them with the ability to obtain more choice options with the same budget as before (Wertenbroch et al., 2020). Despite this, consumers free-will require them to choose between all the options without feeling constrained or being manipulated by the firm's marketing strategies. It is important for the consumers to be able to make decisions on their own, without any external influences which is often applied without consumers’ knowledge and awareness
(Wertenbroch et al., 2020). The lack of awareness and knowledge consumers have about this external influence, might impact their involvement in a purchase and their ability to make a decision based on their own preferences.
Consumers will attempt to exercise autonomy whenever they are trying to make a decision, but might have some constraints as price, time and information (Wertenbroch et al., 2020).
Regardless of their ability to control the outcome, they can choose to play the game based on a mutual exchange whereby businesses and consumers trade products and services for money (Anker, 2020). This exchange is valid as long as both parties are aware and understand the exchange, however consumers can feel that there is a lack of information and not being able to make the best decisions. Being able to be in control of one's own identity, ability to act independently and to some extent be able to control its environment choosing what will fulfill one´s needs (Oyedele & Simpson, 2007). This includes being able to choose identity-relevant products to express who they are and how they want to be perceived by others (Berger &
Heath, 2007).
All consumers will feel the need for autonomy and be in control of its choices, but how individuals perceive autonomy varies. The term was defined by Hertz in 1996 as a state of
own needs and goals (Hertz, 1996). Perceived autonomy is a subjective experience and may be nuanced and vary in salience and intensity (Wertenbroch et al., 2020). This indicates that all consumers experience their autonomy individually in a decision-making process and emphasize different aspects and perceive autonomy individual over their choices.
2.4 Artificial intelligence (AI)
As we have attempted to introduce digital marketing and the use of tools to influence
consumers, it is important to understand how AI works and can be applied. There are several ways to define artificial intelligence (AI) due to its complexity, but several scientists have attempted to create a definition that encloses this complexity. As early as in 1955, McCarthy defined AI as a problem that made a machine behave in ways that would be called intelligent if a human were so behaving (McCarthy, Minsky, Rochester, & Shannon, 2006). Another definition presented by Rust (2020) of AI is “the use of computerized machinery to emulate capabilities once unique to humans” (Rust, 2020). This indicates that developers of AI are attempting to make technology “think” like human beings, but most importantly AI is a set of technologies that works together to become “intelligent” (Bjørlo et al., 2021). The goal of developing AI is to achieve a level of automation of intelligent behavior (Zhang, Lu, & Jin, 2021).
AI is continuous learning and becoming more “intelligent”, being able to self-learn and improve itself by updating and adding to its knowledge base (Kumar et al., 2021). This technology is able to take complex data, analyze it and find patterns and insights which the human mind would not be able to, having the capability to think and act like humans (Kumar et al., 2021).
2.4.1 Big data
To understand how AI works, it is important to first define big data. Big data is larger, more complex data sets that can be used to reveal patterns, trends and other human behavior (Gentsch, 2018). It refers to datasets whose size it’s beyond the ability of typical database software tools to capture, store, manage, and analyze (Gentsch, 2018). This is gathered through the internet, social media, credit card sensors, mobile phones and so on (Ma & Sun, 2020). Big data has existed for a long time, but the amount has increased immensely with
used to process and get a deeper understanding of consumers, but it can also be used for deep learning to exploit the data further (Gentsch, 2018).
2.4.2 Algorithms
Big data does not add value alone, it is first when it is put into algorithms that the value is created. Algorithm is a process or a set of rules to be followed in calculations or other problem-solving operations, especially performed by a computer (Gentsch, 2018). With the increasing amount of big data, it is important to use algorithms to analyze the data to get value and re-create operational functions (Gentsch, 2018). A perfect algorithm has been adjusted by human engineers to the factors of importance repeatedly until a desired outcome (Kumar et al., 2021). After this point, the algorithm is capable of adjusting the factors of importance, without human interaction (Kumar et al., 2021). Through the years the
algorithms have been developed to solve more complex, unknown problems and will solve it through looking for similar, already solved problems in a known database (Gentsch, 2018).
2.4.3 Machine learning
An important part of an AI is machine learning and is an outcome of the algorithms
combined. Mitchell (1997) defined machine learning as a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E (T. M. Mitchell, 1997). An example to better understand this is if a chess computer program improves its performance in playing chess by experience, by playing as many games as possible and analyzing them (T. M. Mitchell, 1997). It has the possibility to collect, process and analyze huge amounts of data and use this to detect patterns and as a result become better at playing chess (Ma & Sun, 2020). Machine learning is a subset of AI that trains a machine on how to learn by using datasets to develop automated, self-training models and integrate multiple methods such that the machine is able to identify patterns and hidden insights without explicit instructions (Gentsch, 2018).
Machine learning is mostly done through three different ways: supervised learning, unsupervised learning and reinforcement learning (Gentsch, 2018). Supervised learning
proceeds within clearly defined limits, using labeled datasets where the right possible answers are already known (Gentsch, 2018). Unsupervised learning, on the other hand, the system is not given target values labelled in advance. It is used to identify similarities in datasets and form clusters (Gentsch, 2018). Reinforcement learning is by using dynamic programming and supervised learning to solve problems (Gentsch, 2018). The complexity behind AI and machine learning can make it difficult to understand, but the main purpose is that the sets of technologies together will solve problems and give great predictions (Zhang et al., 2021).
2.4.4 Application of AI and recommendation algorithms
AI and machine learning is increasingly used in communication and interaction between businesses and consumers. Chatbots and messaging systems are highly relevant and have a big focus on making communication interfaces more efficient (Gentsch, 2018). A good example of this technology can be found in Amazon's Alexa, Google Home and Apple’s Siri, but is also increasing its popularity on web pages for self-help solutions (Gentsch, 2018). The purpose of this technology is to imitate human conversation and solve problems. In the beginning bots could only answer simple, repetitive questions, but with new and advanced AI and machine learning, bots can now solve more demanding tasks (Gentsch, 2018). In
addition, there is increased use of algorithm-based recommendation systems which is a powerful tool to provide more personal and relevant content. With the high number of products and services offered online, it can be difficult for a consumer to navigate online to find products that will fit the need (Wertenbroch et al., 2020). A recommendation algorithm can easily navigate through the product overload to make the decision-making process easier where consumers have little knowledge or experience with a product group (Wertenbroch et al., 2020). This is why marketers are frequently using recommendation algorithms to help consumers find the information and products that will fit their needs and add value by offering personalized content and services (Wertenbroch et al., 2020).
The recommendation algorithms are based on consumers’ past experience, behaviors, preferences and interests, and gives firms opportunities to offer additional content to better satisfy demands and provide additional buying appeals (Gentsch, 2018). The intention of a recommendation algorithm is therefore to help the consumer in the online decision-making
process and enhance user experience leading to higher customer satisfaction (Gentsch, 2018).
In addition, it makes it easier for marketers to meet the right customer over the right channel at the right time (Gentsch, 2018). But as a result, consumers might not be exposed to options and content that does not correspond with their preferences and interests (Wertenbroch et al., 2020). Recommendation systems are able to provide consumers with personalized services and solutions by learning from previous behavior and from there be able to predict current and future preferences (Zhang et al., 2021).
The great amount of big data and information that consumers leave behind is used by algorithms and machine learning to make personalized content for consumers. The data available are used to become better at predicting which products will fulfill consumer preferences and become better at providing high quality recommendations as more data gets available for machine learning (Gentsch, 2018). It will recommend products or services that fit similar items which the consumer has shown interest for earlier. For example, Netflix can give recommendations to a consumer based on previous watched genres, actors, historical records and so on (Zhang et al., 2021). This way the consumer gets a narrower presentation of their content to better navigate and find something to enjoy in a choice overload. If the consumer then clicks on the recommendation, the system understands that it was able to provide a good recommendation for the consumer (Zhang et al., 2021). This will in turn make the AI learn more about the consumer to provide even higher quality recommendations (Zhang et al., 2021).
2.5 Tech competence
As well as consumers have a need for autonomy when choosing a product to purchase, consumers do also have different demands to online companies than previous generations (Balakrishnan, 2018). More consumers are becoming tech competent and spend more time online research products and services before making an online purchase (Chaffey & Ellis- Chadwick, 2019). In addition, tech competent consumers expect companies to deliver and engage in customer experiences. The term tech savviness can be defined as knowing a lot about modern technology, especially computers ("Tech-savvy," 2021). This can be people educated in technology or those who have acquired technology knowledge through using it, but overall, those who consider themselves to be tech competent are more confident with
technological and innovative solutions. Usually, these consumers have more than just a cursory understanding of technology but comprehend technology well (Swilley, 2019). Tech competent users are more prone to trying out new technology and studies show that this competence might influence how participants interact with recommendation systems (Y. Jin, Cai, Chen, Htun, & Verbert, 2019). For companies, it has therefore become important to develop new technologies, apps and platforms to appear more appealing to tech competent consumers(Y. Jin et al., 2019). These consumers are accessible through multiple digital touch points and there is an expectation that tech savvy consumers will engage more to user-
generated content on companies’ digital platforms. In addition, these consumers are more likely to supplement information they receive online with other sources to achieve greater benefits from online shopping (Balakrishnan, 2018).
2.6 Transparency
As explained earlier, AI and machine learning are using already existing big data to make personalized recommendations. These are often based on consumers behaviors which can be measured such as ratings, clicks, purchases, and matching this with content attributes such as popularity, price and author (Harper et al., 2015). Despite having knowledge about which data that typically are used for recommendations, consumers are rarely given explanations of how their behavior online affects recommendations. It is important for consumers to be able to see what is going on, and understand why these recommendations can fulfill their needs, which is related to the term transparency. Transparency is the possibility for consumers to access information, intentions or behavior that have been intentionally revealed through a process of disclosure (Turilli & Floridi, 2009). The term is tightly linked to “openness” which is a concept framed with positive values such as open data, open source, open code and open access (Larsson & Heintz, 2020). This indicates that consumers' behavior online should be mapped in a way for human understanding, so the consumer is able to see why
recommendations are made for them. The act of making a system knowable or visible can be referred to as algorithmic transparency (Rader, Cotter, & Cho, 2018). This term can be defined as the disclosure of information about algorithms to enable monitoring, checking, criticism, or intervention by interested parties (Diakopoulos & Koliska, 2016). It is believed that transparent algorithms can improve consumers' ability to make informed choices when being exposed to a recommendation system, and that the openness will allow more people to
judge if the system works or not, and if it is appropriate for them as a consumer (Rader et al., 2018).
Businesses and consumers should strive to have meaningful transparency where
recommendations that the consumers receive are based on transparent data and algorithms. A system where it is possible to explain and understand how AI creates recommendations for the individual consumer. Transparency in AI can develop more trustworthy systems where the consumers feel more taken care of (Larsson & Heintz, 2020). Despite this, the reality is opposite where the algorithms are closed and often referred to as “black box”. Black boxes occur when knowledge and processes get baked into the algorithm instead of the engineers and consumers being able to see how the AI learns and improves itself (Pedreschi et al., 2019). This is automated decision making where machine learning uses big data to categorize or group consumers without humans being able to understand how and on what grounds (Pedreschi et al., 2019) Those aware of this issue can ask if they as consumers are being treated fairly and able to make their own decisions and feeling autonomy or if they are exposed to external influence (Mittelstadt, Russell, & Wachter, 2019).
As explained above, more consumers spend time online researching products and services that will fulfill their needs and have new expectations for firm’s ability to deliver satisfying customer journeys (Chaffey & Ellis-Chadwick, 2019). These consumers that see themselves as tech competent have developed skills and confidence with using new age technology. By definition tech competent consumers are those who know a lot about modern technology, which includes artificial intelligence, machine learning and algorithms (Millecamp, Htun, Jin,
& Verbert, 2018). Tech competent consumers might be more aware of the digital footprint they leave behind in social media and online services. This indicates that these consumers might have more knowledge about how recommendations algorithms works and what they are built on. Therefore, high tech competent consumers understand more about the algorithms and the transparency is not as important as for those with low tech competence. On the basis of this, the first hypothesis presented is:
H1: Tech competence is positively related to transparency.
2.7 Privacy awareness
AI and recommendations systems are creating new opportunities and ways for companies to connect with consumers and is continuing to innovate business practices. The growth of machine learning and recommendation algorithms has made it much more important to address the problem related to privacy (Mittelstadt et al., 2019). New technology is being developed and adopted by companies, creating more value for the customer and improving customer loyalty. In the meantime, AI and the use of personal and historical data are
reshaping the risk connected to consumer privacy (G. Z. Jin, 2018). In other words, the same technological developments that have created internet as a marketplace with great potential have also increased the threats towards consumer privacy (Lwin, Wirtz, & Williams, 2007).
Personal information can be referred to as any information relating to an identified or identifiable natural person. The data can be directly linked to a person, such as a name, identification number or location data, but also indirectly linked data i.e., physical,
physiological, genetic, mental, economic, cultural or social identity (DPA, 2018). Protection of personal information is becoming more important, and most people have a theoretical interest in keeping their privacy online and do not want everybody to know their personal information (Pötzsch, 2009). Privacy awareness can summarize to which extent consumers is informed about privacy practices and policies and about how disclosed information is used by marketers (Xu, Dinev, Smith, & Hart, 2008). Despite this, there are concerns related to
people’s awareness around information privacy issues. Studies shows that people tend to act differently online than what they intended to, creating what can be called a “privacy paradox”
(Pötzsch, 2009). The privacy paradox refers to how people will have negative attitudes towards providing personal information to websites will despite this share a lot of information, even though there are no apparent benefit for them (Bjørlo et al., 2021).
A good way to describe consumer privacy is seeing it as a transaction; consumers might want to hide their willingness to pay, while the firm wants to hide their real costs, but they are interdependent (G. Z. Jin, 2018). The developments of new age technology have enabled companies to collect, store, process and use data in a larger scale with less costs. In return, consumers are matched with better products for their needs and demands creating more value for them (G. Z. Jin, 2018). Despite this, the consequences of providing personal information
to a company can have a lot of negative side effects. Individuals do not have all resources available and can forget things, but computers do not forget personal and historical data.
Being aware of information privacy risks can include a consumers understanding about what data is collected by whom and for what purposes, with which third parties this data is shared with, and what corresponding risks and benefits may arise (Pötzsch, 2009). The lack of transparency in recommendation algorithms can prevent the consumers to understand which data that is used in algorithms, but they should have the right to select what personal
information that is to be known to what people (Pötzsch, 2009). Having greater tech competence might indicate that consumers are more aware of the information privacy risks related to using online services, because they know that their online behavior might affect the algorithms and their personalized recommendations. On the basis of this, we purpose the second hypothesis to be:
H2: Tech competence is positively related to consumer privacy awareness
2.8 Identity relevance
As investigated earlier in the literature review, being able to be in control of one’s own
identity, and act independently when making a decision, is important for individuals (Oyedele
& Simpson, 2007). Moreover, people all around the world have a drive to be different and make choices that diverge from others (Berger & Heath, 2007). Consumer will choose products that will help them signal their identity and make choices which depends on the set of people that share the taste (Berger & Heath, 2007).
Identity can be defined as “who a person is, or the qualities of a person or group that make them different from others” ("Identity," 2021). Making choices that diverge from others is an effectively way to communicate their identities and ensure others see this as well. Berger and Heath attempted to explain this as a social process of communication to express who you are as a person and signal an identity to others (Berger & Heath, 2007). Through all time, people have attempted to adopt tastes that distinguish from others to express who they are. For example, kids might feel a strong urge to separate themselves from their parents, or people
want to identify themselves with a certain group by adopting a distinguish taste (Berger &
Heath, 2007).
Identity-relevant products tend to diverge more in certain product ranges than others. If to many people adopt a taste, it can create a negative emotional reaction and it can be a fluctuation of tastes. Studies has shown that people want to feel unique and differentiated from others that do not belong to their group but feeling overly similar to others will make people attempt to behave in ways that make them feel different (Berger & Heath, 2007).
Products we purchase, attitudes we profess, and the preferences we hold can act as signal of an identity and is an effectively way to communicate to the social world who we are and how we want to be perceived. Products can be purchased for what they symbolize, not only for their function and what they do (Berger & Heath, 2007). Studies shows that more consumers prefer personalized goods over mass production, which can be linked to peoples need for signaling their identity (Sheehan & Dommer, 2020). Also, a consumer’s identity might be critical and an influential factor in purchase behavior, because consumers prefer products that are consistent with their identities (Sheehan & Dommer, 2020).
For this reason, it is believed that consumers want to understand why certain products are recommended for them and want control over which products we acquire to be able to show our identity to others. It would therefore be natural that consumers would appreciate
transparency in recommendation algorithms before acquiring products which can be categorized as high identity relevant. Consumers will most likely want more control over those choices that affect their identity. Based on this, the third hypothesis presented is:
H3: Transparency in recommendation algorithms is more important for high identity- relevant products
3. Methodology
In this chapter the process from the beginning to a final master thesis is specified and explained. The chapter will start by outlining the quantitative research method that has been utilized in this thesis and discuss the choice of experimental design. After, the variables, survey structure and respondents will be introduced, lastly the statistical approach will be discussed together with reliability and validity.
3.1 Research design
The purpose of the study indicates which research approach to choose, and the aim of the experiment was to understand how the consumer autonomy is influenced by increased
transparency in the recommendation algorithms. A quantitative research approach is typically used to investigate a particular topic through the measurement of variables in quantifiable terms (Mertler, 2018). This means that it relies on collecting and analyzing numerical data to describe, explain, predict or control variables and phenomena of interest (Gay, Mills, &
Airasian, 2009). It seeks to describe current situations, establish relationships between variables, and attempt to explain causal relationships between variables (M. L. Mitchell &
Jolley, 2012). Based on this, quantitative research follows a well-established process in terms of flexibility and no aspect of the research should emerge during the process (Mertler, 2018).
On the other side, qualitative research can provide more in-depth information about the topic (Johannessen, Christoffersen, & Tufte, 2016). To better understand the phenomena studied, a mixed method approach could have been utilized, but the experiment was conducted with a sample of the population so that the quantifiable insight may be produced (Wilson, 2011).
3.1.1 Experimental design
One of methods in quantitative research approach is experimental design (Mertler, 2018).
This is a particular type of study that allows researchers to make cause-effect statements to establish that the difference in behavior is probably not due to anything other than the manipulated variable (M. L. Mitchell & Jolley, 2012). It allows a researcher to establish different conditions and study if these conditions have an effect on the respondents (Mertler, 2018). In an experiment the cause-effect should be retrieved from the manipulated variable, but equally important is the randomization of the sample (M. L. Mitchell & Jolley, 2012).
Random selection is the process of choosing random individuals for participation, such that every member of the population has an equal chance of being selected to be a member of the sample (Mertler, 2018). Randomly select individuals to participate in the study and assign, is important because it eliminate the risk of random error. Random assignment, in turn, means that every individual who has been randomly selected to participate in the experiment has an equal chance to be assigned to any of the groups (Mertler, 2018). The respondents were divided randomly into the three different conditions: low, medium and high. Random
assignment and random selection are important in experimental design to avoid having other underlying variables explaining the cause-effect, as an elimination of random error. Random
assignment to groups, the term means that every individual who has been randomly selected to participate in the experiment has an equal chance to be assigned to any of the groups. That the respondents were divided randomly into the three different conditions: low, medium and high. To avoid having other underlying variables explaining the cause-effect.
The first process of the master thesis was to review literature and research articles to map the knowledge of the use of AI in marketing. Research articles can provide guidance and create the theoretical framework when choosing variables to include in the experiment, but also with hypothesis testing. For this study, it was important to find research articles which mentioned consumer autonomy and transparency in recommendation algorithms, but also those
describing tech competence. These were found through Google Scholar and Oria, and
included keywords i.e., AI, digital marketing, consumer autonomy, tech competence, privacy awareness and identity-relevance. From this, a literature review was assembled providing a good theoretical framework for the thesis.
The research articles and the literature review provided a research base for the variables that we chose to include in our experiment. Reviewing related literature is important for the quality of the research by understanding how researchers have studied similar phenomena before (Mertler, 2018). From the research articles definitions and earlier reliable scales of how to measure the variables was found and organized in an own document. It was used scales that was previously tested in other studies to ensure that we measured the intended factors, and this would in turn increase the reliability of our experiment. For example, tech competence was defined by previous research articles and had been tested on a sample to ensure the reliability (Millecamp, Htun, Conati, & Verbert, 2019). This indicated that similar questions could be used in our experiment to attempt to correctly measure our respondent’s tech competence.
3.2 Data collection 3.2.1 Sawtooth software
When it comes to data collection, the software program Sawtooth was used to conduct the experiment. Sawtooth software enabled us to make a survey with all the variables and was used to ensure that the respondents were randomly assigned to the three conditions. A
problem with online surveys is that it might gather personal data about the respondents which
later on can be used to identify the respondents. In the beginning of the project, we decided to avoid gathering personal data since this would not add any value to the project. Therefore, to avoid the personal data issue, the survey was constructed in Sawtooth Software where it is possible to tick for “not gather IP-address” to keep it completely anonymous. It was also possible to program that the respondents would be randomly assigned to the three conditions, which is important to avoid random error.
3.2.2 Respondents
The target population was online consumers who are using online services, online shopping or social media. These were targeted because they will most likely have experience with recommendations algorithms and are most likely contributing with big data for the machine learning to use. Therefore, these will most likely be representative for the population (M. L.
Mitchell & Jolley, 2012). To ensure that we would receive enough respondents, the survey sample was collected through Prolific. Prolific is a website to help researchers recruit high quality research participants to take part in the study. It is also possible to filter participants with demographic screeners, to have more relevant respondents. Therefore, we chose this website to improve the quality of our data and be sure that we fulfilled our requirements for the three conditions.
Our goal was to have 50 respondents in each condition, and since all respondents would be shown both identity-relevant and non-identity relevant products, it was a total of three conditions. On the basis of this, we required a minimum of 150 respondents randomly assigned to the three conditions but aspired to a larger sample size to reduce sampling error (M. L. Mitchell & Jolley, 2012). A larger sample will also help balance any random error (Mitchell & Jolley, 2012). After conducting the experiment, it was a total of 268 respondents, where 8 was rejected due to missing data and 33 were rejected due to failed attention check.
3.2.3 Pretest
A pretest was performed to classify identity-relevant and non-identity-relevant products, this was adapted from Berger and Heath (Berger & Heath, 2007). Respondents for the pretest were recruited through Reddit and personal contacts. On Reddit the pretest was shared at the pages r/Samplesize and r/SurveyExchange, to be able to reach those willing to answer
finishing the test. “Shoes” and “dress” were rated the most identity-relevant products, while
“toothpaste” and “detergent” were rated non-identity-relevant products. Shoes and toothpaste were the two products that were chosen to include further in the study. The reason behind this was to make the study as gender neutral as possible to avoid having any underlying variables influence the result.
Table 1: Descriptive statistics of pretest
3.2.4 Pilot study
After constructing a survey in Sawtooth Software, a pilot study was shared on Prolific to a small sample group. The purpose of the pilot study was to test if the random assignment was working properly, and to detect any bugs or errors in the survey. It was also important to test if the Sawtooth software was working well when shared on Prolific. A total of ten
respondents took the pilot study where one was rejected due to failing the attention check.
The pilot study was also shared with friends and family to detect any misunderstandings with sentences or formulations in the survey. This enabled us to discuss our survey with others to revise and improve it further and make it more understandable before releasing the main study.
3.2.5 Main study
After revising and finalizing the survey, the main study was released on Prolific to a greater number of respondents. It was not used any filters, so all users of Prolific was able to enter the survey despite demographics etc. The final survey structure can be seen in appendix 1. In addition to the variables, an attention check was included to detect if the respondents had
been paying attention to the survey or not. After conducting the main study, we had a total of 268 respondents where 33 were rejected due to failing the attention check and eight excluded cases due to missing data. This resulted in more respondents than we initially required, and therefore more data to use in the hypothesis testing.
3.3 Measure assessment and data validity 3.3.1 Data cleaning
After collecting data, it is important to uncover any potential error in the data set and uncover any missing data. The process can be time consuming but is an important first step in any research. First, it was checked for errors in the data set. This was done through performing frequencies and descriptive analysis to detect any errors. The descriptive analysis is presented in chapter 3.5 in table 3. Outliers can be scores that falls outside the range of scores available (Pallant, 2016). It was not found any outliers in these analyses, since the minimum and maximum values were inside the established scales. But it was detected eight (8) missing data. This is based on the actions of the respondents, those missing are respondents who decided to not finish the survey. Missing data has a practical impact with making the sample size smaller (Hair, 2010), but since it was only eight (8) missing data, it is believed to have a rather small impact on the results. In addition, it was detected 33 cases where the respondents had failed the attention check. These cases were excluded from the analysis due to the
uncertainty of the validity of these cases. Respondents who fail attention check use less time to complete experiments and can create noise in the data set, therefore eliminating these respondents might increase statistical power (Oppenheimer, Meyvis, & Davidenko, 2009).
3.3.2 Test of normality
Normality refers to the shape of the data distribution for an individual metric variable and its correspondence to the normal distribution (Hair, 2010). If the variation from the normal distribution is sufficiently large, all resulting statistical tests are invalid, because normality is required to use the f and t statistics (Hair, 2010). The statistical analysis performed in this thesis underline the assumptions of a normally distributed depend variable (Hair, 2010). Test of normality was conducted on all variables through nonparametric tests. In addition,
Kolmogrov-Smirnov’s test of normality was conducted to assess if the variables were normally distributed. A significant level of more than 0.05 indicates normality (Pallant,
2016). In the table below, the results shows that all variables used in this study has a sig. level that is less than 0.05, which indicates that the assumptions of normality are violated. Despite this, there can argued that non-normal distribution often occurs in social science due to the underlying nature of the construct being measured, not the scale (Pallant, 2016). Therefore, it was possible to continue with data analysis.
Table 2: Test of Normality
3.4 Description of variables
The study consists of a handful different variables which aim to explain how transparency in algorithms influences consumer autonomy. Despite this, only a few variables will be used to answer the three hypotheses presented in the previous chapter, because this thesis focuses on how tech competence influences the need for transparency. These variables are carefully picked as key variables to ensure that the study remains narrow and are able to answer the hypotheses (Mertler, 2018). All variables that were included in the experiment is presented in the final survey structure which can be seen appendix 1. To answer the hypotheses introduced in the previous chapter, the following variables will be processed further one in this thesis.
3.4.1 Transparency: experimental conditions
An experiment design has an independent variable and at least one dependent variable. The independent variable can be referred to as the manipulating variable, in our study
transparency was treated as the independent variable. Cramer et al. (2008) found that explaining to consumers why recommendations were made increased acceptance of the recommendation (Cramer et al., 2008). Therefore, three experimental conditions were created with different levels of transparency and the respondents were randomly assigned to the conditions.
In low condition the respondents were given no information about what the recommendation algorithm based its recommendation on but was informed that products were presented to them. In the medium condition more information was provided for the respondents on what the algorithm based its recommendation on. This included activity on the website, i.e., purchase history, items in the shopping cart, recently viewed items and items in the wish list.
The last condition with high transparency, the respondents were informed that the algorithm based its recommendation on activity on company websites as in medium, but also i.e., demographic data, geographical location, interest in social media, Google search history.
How these manipulating conditions were presented to the respondents can be seen in appendix 2.
3.4.2 Transparency: manipulation check
After being exposed to the conditions, respondents were asked to answer the manipulation check. This was measured by three (3) items. The scales and the conditions are adapted from the study of Cramer et al. (2008), and the indicators 1 for “Strongly disagree”, 4 for “Neither agree or disagree'', and 7 for “Strongly agree” (Cramer et al., 2008).
3.4.3 Perceived autonomy
A dependent variable can be defined as the factor that the experiment predicts is affected by the independent variable (M. L. Mitchell & Jolley, 2012). In other words, how consumer autonomy is affected by transparency. Smith, Goldstein and Johnson (2013) referred to consumer autonomy as the right of consumers to make their own decisions (N. C. Smith et al., 2013). In this experiment, how consumers perceive autonomy is used as a dependent variable and the scale was adapted from Chen et al. (Chen et al., 2015) and Michaelsen et al.
(Michaelsen, Johansson, & Hedesström, 2021) with a total of eight (8) items. This was measured by a likert scale 1-7 with the indicators 1 for “Strongly disagree”, 4 for “Neither agree or disagree'', and 7 for “Strongly agree”.
3.4.4 Privacy awareness
Privacy can be referred to as the ability of the individual to personally control information about oneself (H. J. Smith, Milberg, & Burke, 1996). The purpose of this variable is to measure respondents' awareness of privacy and act as a control variable. The scale was adapted from Xu et al. (2008) to measure the overall awareness, with a total of three (3) items (Xu et al., 2008). A likert scale 1-7 was used with the indicators 1 for “Strongly disagree”, 4 for “Neither agree or disagree'', and 7 for “Strongly agree”.
3.4.5 Identity-relevance
Berger and Heath (2007) proposed that consumers tend to make choices that diverge from others to ensure that they can communicate their desired identities (Berger & Heath, 2007).
This was first tested in the pretest to distinguish between identity-relevant and non-identity- relevant products. The scale was adapted from Berger and Heath (2007) with a likert scale 1- 7 with the indicators 1 for “Strongly disagree”, 4 for “Neither agree or disagree'', and 7 for
“Strongly agree”. Two items were asked for both two products, shoes and toothpaste, a total of four (4) items.
3.4.6 Tech competence
Tech competence was an important variable for Millecamp et al. when researching how music recommendations affected Spotify users (Millecamp et al., 2018). Their participants were asked to rate themselves on how confident they felt with using modern technology. This scale was therefore adapted from Millecamp et al. (2018) since it attempted to measure the competence. The respondents were asked to answer two (2) items with a likert scale 1-7 with the indicators 1 for “Not at all competent”, 4 for “Neither incompetent or competent”, and 7 for “Very competent”.
3.4.7 Demographics
Demographics were included in the study as a control variable to map the characteristics of the sample group. First, the respondents were asked to indicate their age on a scale 1-5 with the indicators 1 for “Under 18”, 2 for “18-34”, 3 for “35-49”, 4 for “50-65”, and 5 for “Over 65”. For gender the respondents were asked to indicate their gender choosing between 1 for
“male”, 2 for “female” and 3 for “other”. At last, the respondents were asked to indicate their level of education. This was indicated through a scale 1-6 with 1 for “Some high school”, 2 for “Completed high school”, 3 for “Some college”, 4 for “Completed college”, 5 for “Some graduate studies”, and 6 for “Completed advanced degree”.
3.5 Descriptive statistics
The first analysis that was conducted, was descriptive statistics. This provides more detailed information characteristics of the sample (Pallant, 2016)It provides information about total respondents and reports the central tendency i.e., mean scores, mode and median (Pallant, 2016). The total respondents after cleaning the data are 227, which consist of data from the pilot study and main study.
Table 4 shows the characteristics of the demographics of the sample, it shows the total respondents, distribution of age, gender and education level. The distribution of age extends from under 18 to over 65, which indicates that all age groups is represented, but the mean is 2.21, which means that the average respondent is between 18-34 years. For gender both male, female and those who define themselves as something else, are all represented in the sample.
The distribution of education ranges from “Some high school” to “Completed advanced degree”, with a mean score at 3.87. This indicates a slightly higher educated sample.
Table 3: Descriptive statistics of Demographics
Table 4: Descriptive statistics of main variables
The mean scores represent the average of the data, and is found by summating all the values and divide on the number of cases (Johannessen et al., 2016). The mode is that value, which is most frequently used in the data set, and can be used by all types of data i.e., nominal, ordinal and interval (Johannessen et al., 2016). Standard deviation an index for the extent to which individual scores differ from the mean, a measure of the degree of scatter in the scores (M. L. Mitchell & Jolley, 2012). This is also the most common used method for measuring validity, by taking square root of the summated squared deviations from the mean, divide them by the number of observations minus 1 (Hair, 2010). For the variables it shows that all of them has a high mean, except from low identity relevance which has 2.50, which is as expected.
In addition, the descriptive statistics shows the skewness and kurtosis of the data set. The skewness values measure the symmetry of a distribution, in most instances this is made to be normal distributed (Pallant, 2016). A positively skewed distribution has relatively few large values and tails off to the right, and a negatively skewed distribution has relatively few small values and tails off to the left. In addition, kurtosis offers information about the “peakedness”
of the distribution, this means that it intends to measure the peakedness or flatness of a distribution when compared with a normal distribution (Pallant, 2016). A perfect normal distributed score would these two be equal to 0, but a positive value indicates a relatively
